feature_selection-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>smoker</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>houseowner</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>MOD</th>\n",
       "      <th>monthlyhouseholdincome</th>\n",
       "      <th>target_buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>6787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>79</td>\n",
       "      <td>6915</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>9789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>220</td>\n",
       "      <td>10049</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no</td>\n",
       "      <td>6350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>185</td>\n",
       "      <td>6587</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5000</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>226</td>\n",
       "      <td>5193</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no</td>\n",
       "      <td>5307</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>341</td>\n",
       "      <td>5326</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age            job  marital  education smoker  monthlyincome houseowner  \\\n",
       "0   30     unemployed  married    primary     no           6787         no   \n",
       "1   33       services  married  secondary     no           9789        yes   \n",
       "2   35       services   single   Graduate     no           6350        yes   \n",
       "3   59    blue-collar  married  secondary     no           5000        yes   \n",
       "4   36  self-employed  married   Graduate     no           5307        yes   \n",
       "\n",
       "  loan    contact  MOD  monthlyhouseholdincome target_buy  \n",
       "0   no  cellphone   79                    6915        yes  \n",
       "1  yes  cellphone  220                   10049        yes  \n",
       "2   no  cellphone  185                    6587        yes  \n",
       "3   no    unknown  226                    5193        yes  \n",
       "4   no  cellphone  341                    5326        yes  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the dataset\n",
    "df = pd.read_csv(\"output.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Chi square test \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class ChiSquare:\n",
    "    def __init__(self, dataframe):\n",
    "        self.df = dataframe\n",
    "        self.p = None #P-Value\n",
    "        self.chi2 = None #Chi Test Statistic\n",
    "        self.dof = None\n",
    "        \n",
    "        self.dfTabular = None\n",
    "        self.dfExpected = None\n",
    "        \n",
    "    def _print_chisquare_result(self, colX, alpha):\n",
    "        result = \"\"\n",
    "        if self.p<alpha:\n",
    "            result=\"{0} is IMPORTANT for Prediction\".format(colX)\n",
    "        else:\n",
    "            result=\"{0} is NOT an important predictor. (Discard {0} from model)\".format(colX)\n",
    "\n",
    "        print(result)\n",
    "        \n",
    "    def TestIndependence(self,colX,colY, alpha=0.05):\n",
    "        X = self.df[colX].astype(str)\n",
    "        Y = self.df[colY].astype(str)\n",
    "        \n",
    "        self.dfObserved = pd.crosstab(Y,X) \n",
    "        chi2, p, dof, expected = stats.chi2_contingency(self.dfObserved.values)\n",
    "        self.p = p\n",
    "        self.chi2 = chi2\n",
    "        self.dof = dof \n",
    "        \n",
    "        self.dfExpected = pd.DataFrame(expected, columns=self.dfObserved.columns, index = self.dfObserved.index)\n",
    "        \n",
    "        self._print_chisquare_result(colX, alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>smoker</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>houseowner</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>MOD</th>\n",
       "      <th>monthlyhouseholdincome</th>\n",
       "      <th>target_buy</th>\n",
       "      <th>dummyvar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>6787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>79</td>\n",
       "      <td>6915</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>9789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>220</td>\n",
       "      <td>10049</td>\n",
       "      <td>yes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no</td>\n",
       "      <td>6350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>185</td>\n",
       "      <td>6587</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5000</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>226</td>\n",
       "      <td>5193</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no</td>\n",
       "      <td>5307</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>341</td>\n",
       "      <td>5326</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age            job  marital  education smoker  monthlyincome houseowner  \\\n",
       "0   30     unemployed  married    primary     no           6787         no   \n",
       "1   33       services  married  secondary     no           9789        yes   \n",
       "2   35       services   single   Graduate     no           6350        yes   \n",
       "3   59    blue-collar  married  secondary     no           5000        yes   \n",
       "4   36  self-employed  married   Graduate     no           5307        yes   \n",
       "\n",
       "  loan    contact  MOD  monthlyhouseholdincome target_buy  dummyvar  \n",
       "0   no  cellphone   79                    6915        yes         1  \n",
       "1  yes  cellphone  220                   10049        yes         0  \n",
       "2   no  cellphone  185                    6587        yes         1  \n",
       "3   no    unknown  226                    5193        yes         1  \n",
       "4   no  cellphone  341                    5326        yes         1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adding dummy variable for testing\n",
    "df['dummyvar'] = np.random.choice([0,1], size=(len(df),),p=[0.5,0.5])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age is IMPORTANT for Prediction\n",
      "job is IMPORTANT for Prediction\n",
      "marital is IMPORTANT for Prediction\n",
      "smoker is NOT an important predictor. (Discard smoker from model)\n",
      "monthlyincome is IMPORTANT for Prediction\n",
      "houseowner is IMPORTANT for Prediction\n",
      "loan is IMPORTANT for Prediction\n",
      "contact is IMPORTANT for Prediction\n",
      "MOD is IMPORTANT for Prediction\n",
      "monthlyhouseholdincome is IMPORTANT for Prediction\n",
      "dummyvar is NOT an important predictor. (Discard dummyvar from model)\n"
     ]
    }
   ],
   "source": [
    "cT=ChiSquare(df)\n",
    "#feature Selection output\n",
    "testColumns = ['age','job','marital','smoker','monthlyincome','houseowner','loan','contact','MOD','monthlyhouseholdincome','dummyvar']\n",
    "\n",
    "for var in testColumns:\n",
    "    cT.TestIndependence(colX=var,colY=\"target_buy\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>smoker</th>\n",
       "      <th>monthlyincome</th>\n",
       "      <th>houseowner</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>MOD</th>\n",
       "      <th>monthlyhouseholdincome</th>\n",
       "      <th>target_buy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>6787</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>79</td>\n",
       "      <td>6915</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>9789</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>220</td>\n",
       "      <td>10049</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no</td>\n",
       "      <td>6350</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>185</td>\n",
       "      <td>6587</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5000</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>226</td>\n",
       "      <td>5193</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>no</td>\n",
       "      <td>5307</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellphone</td>\n",
       "      <td>341</td>\n",
       "      <td>5326</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age            job  marital  education smoker  monthlyincome houseowner  \\\n",
       "0   30     unemployed  married    primary     no           6787         no   \n",
       "1   33       services  married  secondary     no           9789        yes   \n",
       "2   35       services   single   Graduate     no           6350        yes   \n",
       "3   59    blue-collar  married  secondary     no           5000        yes   \n",
       "4   36  self-employed  married   Graduate     no           5307        yes   \n",
       "\n",
       "  loan    contact  MOD  monthlyhouseholdincome target_buy  \n",
       "0   no  cellphone   79                    6915        yes  \n",
       "1  yes  cellphone  220                   10049        yes  \n",
       "2   no  cellphone  185                    6587        yes  \n",
       "3   no    unknown  226                    5193        yes  \n",
       "4   no  cellphone  341                    5326        yes  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing the dummy variable\n",
    "df.drop('dummyvar',axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ee9b0964e0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdQAAAF0CAYAAACaO8iCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSpI0AwtVkqQZWKiSJM3gH4bFs3+hOQRiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee9b091f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#producing the heatmap for correlations among numerical variables\n",
    "tc = df.corr()\n",
    "sns.heatmap(tc,annot=True, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "link-\n",
    "http://www.insightsbot.com/blog/2AeuRL/chi-square-feature-selection-in-python\n",
    "\n",
    "'''"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}